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AUTO3D: Novel View Synthesis Through Unsupervisely Learned Variational Viewpoint and Global 3D Representation

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Computer Vision – ECCV 2020 (ECCV 2020)

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Abstract

This paper targets on learning-based novel view synthesis from a single or limited 2D images without the pose supervision. In the viewer-centered coordinates, we construct an end-to-end trainable conditional variational framework to disentangle the unsupervisely learned relative-pose/rotation and implicit global 3D representation (shape, texture and the origin of viewer-centered coordinates, etc.). The global appearance of the 3D object is given by several appearance-describing images taken from any number of viewpoints. Our spatial correlation module extracts a global 3D representation from the appearance-describing images in a permutation invariant manner. Our system can achieve implicitly 3D understanding without explicitly 3D reconstruction. With an unsupervisely learned viewer-centered relative-pose/rotation code, the decoder can hallucinate the novel view continuously by sampling the relative-pose in a prior distribution. In various applications, we demonstrate that our model can achieve comparable or even better results than pose/3D model-supervised learning-based novel view synthesis (NVS) methods with any number of input views.

X. Liu, T. Che and Y. Lu—Contribute Equally.

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Acknowledgments

This work was supported by the Jangsu Youth Programme [grant number SBK2020041180], National Natural Science Foundation of China, Younth Programme [grant number 61705221], the Fundamental Research Funds for the Central Universities [grant number GK2240260006], NIH [NS061841, NS095986], Fanhan Technology, and Hong Kong Government General Research Fund GRF (Ref. No.152202/14E) are greatly appreciated.

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Authors and Affiliations

  1. HMS, Harvard University, Boston, MA, 03315, USA

    Xiaofeng Liu

  2. MILA, Université de Montréal, Montréal, Canada

    Tong Che

  3. Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Yiqun Lu

  4. Fanhan Tech. Inc., Suzhou, 215128, China

    Xiaofeng Liu

  5. Facebook AI, Boston, MA, 03315, USA

    Chao Yang

  6. Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    Site Li

  7. Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong

    Jane You

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  1. Xiaofeng Liu
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  2. Tong Che
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  4. Chao Yang
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  5. Site Li
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  6. Jane You
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Correspondence to Xiaofeng Liu .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Liu, X., Che, T., Lu, Y., Yang, C., Li, S., You, J. (2020). AUTO3D: Novel View Synthesis Through Unsupervisely Learned Variational Viewpoint and Global 3D Representation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12354. Springer, Cham. https://doi.org/10.1007/978-3-030-58545-7_4

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